Images are used to evaluate, review and track physical phenomena. If an acquired image turns out to be unacceptable for its intended purpose (e.g., too blurry) it may be inefficient or impossible to re-acquire a suitable image at a later time. This issue is particularly troublesome in the case of magnetic resonance (MR) imaging, due to long image acquisition times (leading to patient movement and resulting image artifacts) and costs of re-acquisition. The inclusion of image quality assessment in an imaging pipeline may therefore improve system efficiency by ensuring sufficient quality of acquired images.
Quality assessment of medical images may assess whether image quality is acceptable or unacceptable for diagnostic purposes. Prior assessment systems have attempted to define image features which may indicate whether an image is acceptable or unacceptable. These systems have not proved suitable, at least in part to the large variety of ways in which an image may be unacceptable, and the challenge of encoding clinical knowledge relating to image quality assessment using conventional image processing techniques and metrics.
Since the determination of whether or not an image is of acceptable quality is a two-class classification task, some image quality assessment systems utilize discriminative learning models. However, these systems also have difficulty in dealing with image artifact variations, which are difficult to exhaustively define and may appear globally or locally. The number of available training samples is also limited. Moreover, these systems are unable to account for differences in image quality perception among different clinical personnel.